tf.contrib.graph_editor.reroute_b2a_inputs(sgv0, sgv1) Re-route all the inputs of sgv1 to sgv0 (see reroute_inputs).

class tf.nn.rnn_cell.InputProjectionWrapper Operator adding an input projection to the given cell. Note: in many cases it may be more efficient to not use this wrapper, but instead concatenate the whole sequence of your inputs in time, do the projection on this batch-concatenated sequence, then split it.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.distributions.MultivariateNormalDiag.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). Additional documentation from _MultivariateNormalOperatorPD: x is a batch vector with compatible shape if x is a Tensor whose shape can be broadcast up to either: self.batch_shape + self.event_shape or [M1,...,Mm] + self.batch_shape + self.event_shape Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a

tf.nn.rnn_cell.BasicRNNCell.__init__(num_units, input_size=None, activation=tanh)

tf.parse_example(serialized, features, name=None, example_names=None) Parses Example protos into a dict of tensors. Parses a number of serialized Example protos given in serialized. example_names may contain descriptive names for the corresponding serialized protos. These may be useful for debugging purposes, but they have no effect on the output. If not None, example_names must be the same length as serialized. This op parses serialized examples into a dictionary mapping keys to Tensor and Sp

tf.contrib.learn.monitors.RunHookAdapterForMonitors.after_run(run_context, run_values)

tf.contrib.learn.monitors.CaptureVariable.every_n_step_begin(step)

tf.contrib.learn.monitors.CheckpointSaver.step_end(step, output) Callback after training step finished. This callback provides access to the tensors/ops evaluated at this step, including the additional tensors for which evaluation was requested in step_begin. In addition, the callback has the opportunity to stop training by returning True. This is useful for early stopping, for example. Note that this method is not called if the call to Session.run() that followed the last call to step_begin()